Edge intelligence-based resource modification for transmitting data streams to a provider network

ABSTRACT

A trained model and/or an edge client running on an edge device may obtain data from a data source (e.g., a security video camera) and determine, based on a result of processing the data using the model, whether to send an indication of an upcoming data/video stream to the provider network (e.g., indicating a bearer modification). The received indication may be used by the provider network to send a request to a serving wireless infrastructure (e.g., telco operator/wireless mobile core) for configuration of one or more resources on behalf of the edge device to process the upcoming data stream. The received indication may be used by the provider network in order to configure one or more resources at the provider network to process the upcoming data stream. The edge device initiates transmission of the data stream from the data source to the provider network via the serving wireless infrastructure.

This application is a continuation of U.S. patent application Ser. No.16/835,097, filed Mar. 30, 2020, which is hereby incorporated byreference herein in its entirety.

BACKGROUND

The demand for transmission and processing of data streams continues togrow as individuals and organizations take advantage of the capabilitiesoffered by communication providers (e.g., internet providers, telcooperators) and web service providers (e.g., via provider networks thatprovide “cloud” computing). For example, the global video surveillanceindustry uses these technologies to provide video monitoring andsecurity services, and this industry is expected to grow significantlyin the coming years.

As wireless standards (e.g., 3G, 4G, 5G) and technologies continue toevolve, the processing of data streams (e.g., time-encoded data such asvisual data and/or non-visual data—radar, mm wave, etc.) by wirelessinfrastructures and cloud companies to connect next-generation edgedevices (e.g., internet of things (IoT) devices) with IP mobilitynetworks can introduce complex and challenging problems. Therefore, itcan be difficult to develop a next-generation (4G, 5G, etc.) enabledframework that provides a desired time to respond (e.g., to meetexpectations of low latency) and that also efficiently implements therouting/processing of data streams (e.g., video and/or othertime-encoded data) that introduce a much higher demand for bandwidth,storage, and analytics capability.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

FIG. 2A is a logical block diagram illustrating throughput fortransmission of a video stream over a period of time.

FIG. 2B is a logical block diagram illustrating throughput fortransmission of a video stream over a period of time in a system usingedge intelligence-based resource modification, according to someembodiments.

FIG. 3 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

FIG. 4 is a logical block diagram illustrating an edge device that isused for edge intelligence-based resource modification to transmit datastreams to a provider network, according to some embodiments.

FIG. 5 is a logical block diagram illustrating an edge device that isused for edge intelligence-based resource modification to transmit datastreams to a provider network, according to some embodiments.

FIG. 6 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

FIG. 7 is a high-level flowchart illustrating various methods andtechniques to implement model deployment for a system for edgeintelligence-based resource modification to transmit data streams to aprovider network, according to some embodiments.

FIG. 8 is a high-level flowchart illustrating various methods andtechniques to implement a system for edge intelligence-based resourcemodification for transmitting data streams to a provider network,according to some embodiments.

FIG. 9 is a high-level flowchart illustrating various methods andtechniques to implement an edge device for edge intelligence-basedresource modification to transmit data streams to a provider network,according to some embodiments.

FIG. 10 is a high-level flowchart illustrating various methods andtechniques to manage multiple edge devices in a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

FIG. 11 is a block diagram illustrating an example computing system,according to some embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that the embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include”, “including”, and“includes” mean including, but not limited to.

DETAILED DESCRIPTION

The systems and methods described herein may be employed in variouscombinations and in various embodiments to implement edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments. In embodiments,using edge intelligence-based resource modification for transmittingdata streams to a provider network may optimize usage of the wirelessspectrum and provider network resources by reducing the amount ofunnecessary data stream transmissions from data sources (e.g., videocameras or microphones) to a provider network, which may also reduce theamount of compute/storage resources used.

Embodiments described herein may result in cost savings for a providernetwork, its clients, and a telco operator (e.g., a cellular carrier)that provides a serving wireless infrastructures for transmission ofdata streams (due to reduced bandwidth usage, reduced resource usage,etc.). As used herein, the term “provider network” refers to a cloudand/or edge infrastructure of a service provider (e.g., provider of theABM service and/or other services to clients as discussed herein), andis different than the serving wireless infrastructure of a telcooperator (e.g., the carrier that provides wireless/cellular-relatedfunctions to transmit data streams on behalf of edge devices, such assubscriber management, analytics, etc.).

In embodiments, a trained model and/or an edge client running on an edgedevice may obtain data from a data source (e.g., a security videocamera) and determine, based on a result of processing the data usingthe model, whether to send an indication of an upcoming data stream tothe provider network (e.g., indicating a bearer modification). Inembodiments, the indication may be used by the provider network to senda request to a serving wireless infrastructure (e.g., a wireless mobilecore or other data stream-processing infrastructure/network of a telcooperator that provides wireless/cellular-related functions to transmitdata on behalf of subscribing edge devices using one or more licensedand/or unlicensed frequencies) for configuration of one or moreresources on behalf of the edge device.

In some embodiments, the indication may be used by the provider networkin order to configure one or more resources at the provider network(instead of or in addition to sending a request to configure a servingwireless infrastructure). In some embodiments, the indication may beused by the provider network in order to cause an edge device/datasource device to be configured (e.g., to transmit a data stream at aparticular quality level). After the resources of the serving wirelessinfrastructure and/or provider network and/or the edge device/datasource device are configured, the edge device may initiate transmissionof the data stream from the data source to the provider network via theserving wireless infrastructure.

As an example, detection of a cat (non-suspect object or animal) using amodel for video data processing may not trigger the edge device to sendan indication to the provider network of an upcoming video stream, butdetection of a person as a possible intruder (suspect individual) maytrigger the edge device to send an indication of an upcoming videostream (e.g., indicating a bearer modification to transmit/process thevideo stream). Based on the received indication, the provider networkmay cause various video stream-processing resources (e.g., analytics andcompute resources of the provider network and/or a serving wirelessinfrastructure) to be configured in order to process the upcoming videostream.

Although video data and other types of data are discussed herein, invarious embodiments any other type of data may be generated by any typeof sensor, processed by one or more models, and/or transmitted as a datastream. For example, the time-encoded data of a data stream may includevideo, audio, light detection and ranging (LIDAR), millimeter wave,thermal, infrared, or any other visual and/or non-visual data. As usedherein with respect to a video stream, to “detect” an object/subject maybe equivalent to identifying (e.g., using object recognition) anobject/subject in the video stream. In embodiments, object recognitionrefers to a collection of one or more computer vision tasks (e.g.classification, detection, segmentation, localization, etc.) implementedby one or more models (e.g., using data of a video stream as input) toidentify one or more objects, persons, faces and/or other items ofrelevance in a data stream (e.g., video stream). For example, a modelmay detect (identify) a suspect individual by implementing one or moretasks to analyze/process data of a video stream provided by a videocamera.

In embodiments, a model running on an edge device may obtain data fromany number/type of data sources to process and generate a result. Forexample, a model may obtain the data from any number of different videocameras and/or microphones, process the video and/or audio data, andgenerate a result. Thus, the input data may represent a fusion orcombination of any number and any type of data sources/sensors.

In various embodiments, the components illustrated in the figures may beimplemented directly within computer hardware, as instructions directlyor indirectly executable by computer hardware (e.g., a microprocessor orcomputer system), or using a combination of these techniques. Forexample, the components of the figures may be implemented by a systemthat includes one or more computing nodes, in one embodiment, each ofwhich may be similar to the computer system embodiment illustrated inFIG. 11 and described below.

This specification begins with a description of using edgeintelligence-based resource modification for transmitting data streamsto a provider network. A number of different methods and techniques toimplement edge intelligence-based resource modification for transmittingdata streams to a provider network are discussed, some of which areillustrated in accompanying flowcharts. Finally, a description of anexample computing system upon which the various components, modules,systems, and/or techniques described herein may be implemented isprovided. Various examples are provided throughout the specification.

FIG. 1 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

In the depicted embodiment, a provider network 102 includes anartificial-intelligence based bearer modification (ABM) service 104 thatimplements edge intelligence-based resource modification fortransmitting data streams to the provider network. Based on receiving anindication from an edge device 106, the ABM service may modify (e.g.,configure) resources 108 of a serving wireless infrastructure 110 and/orresources 112 of the provider network 102.

In the example embodiment, the serving wireless infrastructure 110 isremotely located from the provider network 102 and from the edge devices106 (e.g., in a local (e.g., private) network of a telco operatorseparate from a local network of the provider network), and maycommunicate with the provider network (or edge devices) via a wide-areanetwork (e.g., the Internet). Similarly, the edge devices 106 areremotely located from the provider network 102 and the serving wirelessinfrastructure 110 (e.g., in a local network of a client (e.g., clientof the provider network and/or telco operator) and may communicate withthe provider network (or the serving wireless infrastructure) via thewide-area network (e.g., the Internet).

In various embodiments, at least some of the provider network 102 mayinclude and/or control one or more components within/hosted by theserving wireless infrastructure 110. Similarly, at least some of theserving wireless infrastructure 110 may include and/or control one ormore components within/hosted by the provider network 102. In someembodiments, at least some components/portions of the serving wirelessinfrastructure 110 may be considered part of the provider network (andvice-versa). Therefore, in various embodiments, any of the datatransport functions and/or data processing functions described hereinthat are performed by the provider network may instead be performed bythe serving wireless infrastructure (and vice-versa).

Although hardware/software components controlled by the provider networkmay be physically located at a site/data center of the serving wirelessinfrastructure, those hardware/software components may be considered aspart of the provider network (e.g., part of the same logical providernetwork that includes components physically located at the site/datacenters of the provider network and the serving wirelessinfrastructure). Similarly, even though hardware/software componentscontrolled by the serving wireless infrastructure may be physicallylocated at a site/data center of the provider network, thosehardware/software components may be considered as part of the servingwireless infrastructure (e.g., part of the same logical network of theserving wireless infrastructure that includes components physicallylocated at the site/data centers of the serving wireless infrastructureand the provider network).

In the depicted embodiment, a given edge device 106 may include an edgeclient 114. The edge client may include any number of data processingmodels 116. In embodiments, any number of the models 116 may be trainedby the provider network and/or provided by the provider network. Forexample, the ABM service may cause the provider network to deploy/sendmodels to an edge device. As depicted, there may be any number of edgedevices 106. Furthermore, any number of clients of the provider networkmay use the ABM service, each with any number of their own edge devices.For example, one client may have local network that includes 100different edge devices, another client may have a local network thatincludes 2000 different edge devices, and another client may client mayhave just one stand-alone edge device (e.g., a mobile device such as asmart phone).

In embodiments, a given model of an edge device (e.g., a model 116 a)may trigger the sending of an indication 118 of an upcoming data streamto the provider network. For example, the model may obtain video data(e.g., triggering input 120) received from sensor(s) of a video cameraand process the obtained data by a trained model to generate a result(e.g., a classification of a detected object based on at least a portionof the video data).

In embodiments, a result of a model may be a prediction based on dataprocessed by the model. For example, a model may generate a predictionthat a detected object/subject is a cat (e.g., instead of a dog or aperson). In embodiments, the prediction may include a confidence levelor accuracy assigned to the prediction. For example, the model maypredict an object is a cat and assign a confidence level or accuracy of95%, indicating a relatively high level of confidence in the prediction.In some embodiments, a prediction may be an event or an event that willhappen in the future. For example, based on analyzing a video stream, amodel may predict, with 90% accuracy, that a car will hit another car(e.g., based on identifying each car and analyzing the location andspeed of each car).

In some embodiments, the edge device may determine, based on the result,whether to send an indication 118 of an upcoming data stream to theprovider network. If the result indicates that a suspect individual wasdetected by the model (e.g., at least a portion of the data wasclassified by the model as a suspect individual), then the edge devicemay determine, based on application of one or more rules, to generate anindication of an upcoming data stream and send it to the ABM service. Ifthe result indicates that a non-suspect individual or animal wasdetected by the model (e.g., at least a portion of the data wasclassified by the model as a non-suspect individual or animal), then theedge device may determine, based on application of the one or morerules, not to generate an indication of an upcoming data stream. Thus,the model may apply any number of rules for any number of model results(e.g., different subjects/objects, etc.) in order to determine whetherto generate an indication of an upcoming data stream (and one or moreparameters of the data stream, such as quality).

In various embodiments, the indication may include a quality of theupcoming data/video stream (e.g., quality of service or QoS), aconfiguration of one or more resources of the provider network or theserving wireless infrastructure to process the data stream, and/or anidentifier for the edge device. In some embodiments, the quality of thedata stream may specify a frame rate (e.g., number of frames per secondor fps), a type of data encoding used, an amount of packet loss,latency/delay, or jitter that may tolerated (between the datasource/edge device and the provider network/destination service),bandwidth available for transmission, an amount/type of compressioncodec (e.g., H264), resolution (e.g., HD, 4K, 8K), etc. In embodiments,the one or more rules may be provided to the edge device as part of themodel or provided separately for use with the model.

Based on the indication received from the edge device, the ABM servicemay send, to an interface (e.g., an application programming interface(API)) of a serving wireless infrastructure), a request 122 to configureone or more resources of the serving wireless infrastructure (e.g.,analytics, subscriber management, etc.) on behalf of the edge device inorder to transmit a data stream (e.g., video stream) from the datasource to the provider network via the serving wireless infrastructure.In embodiments, to transmit the data stream from the data source deviceto the provider network, the data stream may be received from the datasource at the edge device and transmitted from the edge device to theprovider network via the serving wireless infrastructure. In someembodiments, the data source itself (e.g., the video camera) maydirectly transmit the data stream to the provider network via theserving wireless infrastructure (e.g., without going through the edgedevice, by using a transmitter of the video camera itself).

In some embodiments, based on the indication received from the edgedevice, the ABM service may configure 124 one or more resources of theprovider network (e.g., compute, storage, etc.) to process the datastream. In embodiments, the ABM service may determine, based on theindication from the edge device, a quality of the data stream to betransmitted (e.g., QoS) and configure one or more resources of theprovider network (and/or the wireless infrastructure) to process thedata stream according to the determined quality.

In some embodiments, the ABM service may also send another indication tothe edge device or the data source, wherein the other indication causesthe data source to transmit the data stream according to the determinedquality. For example, the other indication may include an instruction,command, and/or other data that the edge device or data source uses inorder to configure the data source to transmit the data stream accordingto the determined quality (e.g., at a particular frame rate, resolution,and/or compression type/level). For example, the configuration/mode ofthe data source by be changed from lower quality transmission (e.g.,lower frame rate and/or lower resolution) to higher quality transmission(e.g., higher frame rate and/or higher resolution).

This may allow a provider network (e.g., via the ABM service) tocoordinate various aspects of the data stream transmission (e.g.,quality, timing, etc.) from any number of data sources by controllingedge devices/data source device as well as the data transmissionresources at the telco operator's network (the serving wirelessinfrastructure). For example, the ABM service may determine, based on aparticular license plate number that was detected, that the car isflagged as stolen (e.g., based on a database at the provider network).In response, the ABM service may send an indication to the edge deviceor the video camera to cause the video camera to transmit the videostream according to a higher frame rate and/or resolution than thecurrently configured frame rate and/or resolution. This may allow theABM service and/or the edge device model to perform facial recognitionon the driver and/or passengers in order to identify who is in thestolen car. Moreover, the ABM service may configure other nearby cameras(e.g., within a threshold distance of the video camera or on the samestreet) to provide additional video streams at the higher frame rateand/or resolution.

After the resources of the provider network and/or the serving wirelessinfrastructure are configured to process the upcoming data stream basedon the indication (e.g., according to the level of quality and/or anyother parameters for the data stream provided by the indication).

If the result indicates that a non-suspect individual or non-suspectanimal/object was detected by the model, then the edge device maydetermine, based on application of the one or more rules, not togenerate an indication of an upcoming data stream because the clientdoes not have a desire to use or record non-suspect individuals/objects.This may reduce usage of resources of the client, the provider network,and/or the wireless infrastructure, resulting in reduced costs fortransmission of data and increased life of transmission/storageequipment.

In embodiments, the result of the model may be transmitted to theprovider network (e.g., without applying any rules). In this case, theprovider network may generate, based on application of one or more rulesassigned to the model (e.g., the rules are stored/known to the ABMservice), an indication of an upcoming data stream to be transmittedfrom the edge device. In this embodiment, less bandwidth may be usedbecause only the result of the model is transmitted (e.g., a singledigit such as “1” or “7” that indicate different levels of transmissionquality). As mentioned above, in various embodiments the indication mayinclude a quality of the upcoming data/video stream (e.g., quality ofservice or QoS), a configuration of one or more resources of theprovider network and/or the serving wireless infrastructure to processthe data stream, and/or an identifier for the edge device. In someembodiments, the configuration of the resources of the provider networkand/or serving wireless infrastructure may include a type of resource,identification of one or more resources, a number of resources/serviceinstances to instantiate (e.g., ingestion instances, storage instances),and/or any other number of parameters used by the provider networkand/or serving wireless infrastructure to configure resources.

As mentioned above, based on the generated indication, the ABM servicemay then send, to an interface of a serving wireless infrastructure, arequest to configure one or more resources of the serving wirelessinfrastructure on behalf of the edge device in order to transmit a datastream from the data source to the provider network via the servingwireless infrastructure and/or configure one or more resources of theprovider network. The ABM service may determine, based on the generatedindication from the edge device, a quality of the data stream to betransmitted (e.g., QoS) and configure one or more resources of theprovider network (and/or the wireless infrastructure) to process thedata stream according to the determined quality. After the resources ofthe provider network and/or the serving wireless infrastructure areconfigured to process the upcoming data stream based on the indication(e.g., according to the level of quality and/or any other parameters forthe data stream provided by the indication), the data source may begintransmitting the data stream.

In embodiments, an edge device/data source may no longer be collectingdata that is of interest to a client and therefore the transmission ofthe data stream may terminated. For example, the edge device determine,based on a result of a model, that a suspect individual is no longerdetected in a video feed and therefore the edge device may send anindication to the ABM service that includes the result of the modeland/or instructs the ABM service to terminate the transmission of thedata stream and/or release one or more of the resources of the servingwireless infrastructure and/or the provider network that are being usedfor transmission of the data stream.

In some embodiments, the ABM service may receive, from the edge device,another indication based on another result generated by the trainedmodel. Based on the other indication, the ABM service may send, to theinterface of the serving wireless infrastructure, another request torelease one or more resources of the serving wireless infrastructure, sothey become available to be configured for transmission of other datastreams. The ABM service may also release on or more resources of theprovider network, so they become available to be configured forprocessing of other data streams.

FIG. 2A is a logical block diagram illustrating throughput fortransmission of a video stream over a period of time. In this example, avideo camera capable of SD/HD quality transmission is shown. However,any other type of camera with any other quality capability may be used.

As shown, the video camera at a client's network/site captures data(e.g., images and/or audio data) over a period of time 202. The videocamera may transmit video data to a destination for storage and/oranalysis (e.g., to a web service provider) that may not be desired orneeded by the client. For example, the video camera may detect changesin light or movement of a cloud, balloon, leaves, animals, or anauthorized person/owner of the camera and transmit the correspondingvideo data, even though the client may have no desire to obtain, store,or analyze such data.

The video camera in this example may detect a suspect individual orobject and transmit the corresponding video data. However, since thevideo camera is not used in as part of a system for edgeintelligence-based resource modification, a large amount of unnecessaryor unwanted video transmission occurs, resulting in unnecessary usage orwaste of various transmission, storage, and/or other resources. Forexample, some cameras close to a radio network may consume all or mostof the bandwidth available to the client, which may result in a higherdata rate plan and/or overage charges for unnecessary and/or unwantedvideo transmission. Furthermore, this may result in other cameras (e.g.,further away from the radio/cellular network) having little or nobandwidth available for transmission.

FIG. 2B is a logical block diagram illustrating throughput fortransmission of a video stream over a period of time in a system usingedge intelligence-based resource modification, according to someembodiments. In the example embodiment, a video camera capable of SD/HDquality transmission or HD/4K/8K quality transmission (with dynamicvideo quality adaptation) is shown. However, in various embodiments, anyother type of camera with any other quality capability may be used.

In the example embodiment, an edge device (e.g., edge device 106)includes the video camera or the edge device may communicate with thevideo camera (e.g., via wired or wireless communication) so that theedge device obtains video data and processes the video data (e.g., by amodel 116). As shown, the video camera at a client's network/sitecaptures data (e.g., images and/or audio data) over a period of time204. A model may obtain video data corresponding to changes in light ormovement of a cloud, balloon, leaves, animals, or an authorizedperson/owner of the camera. As described herein, the detection of any ofthe above may result in no (or lower quality such as SD/HD) transmissionof video data to a provider network (e.g., using one or more rules forlighting or objects/individuals classified as non-suspect) because theclient may have no desire to store and/or analyze this classification ofdata. As described herein, this avoids unnecessary usage of resources.

As shown, the model may obtain video data corresponding to a suspectindividual or object and transmit the corresponding video data. Asdescribed herein, the detection of a suspect individual or object mayresult in transmission of higher quality (e.g., HD/4K/8K) video data tothe provider network (e.g., using one or more rules forobjects/individuals classified as suspect individuals or objects)because the client may have a desire to store and/or analyze thisclassification of data using the higher quality video data. As depicted,the cameras may consume less bandwidth due to implementing edgeintelligence-based resource modification, which may result a lower datarate plan because the client avoids unwanted video transmissions. Asshown, this may result in multiple cameras having access to moreavailable bandwidth (e.g., cameras will get their requested bandwidth,regardless of their location with respect to the radio/cellularnetwork).

FIG. 3 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

As depicted, a provider network (e.g., provider network 102) includes anABM service (ABM service 104), as well as various other services thatmay be used to process data streams. For example, a video stream servicemay receive and perform initial processing of a video stream andanalytics service may perform various types of analysis on the videostream. A machine learning (ML) service may be used to train models tobe deployed to edge devices (e.g., edge device 106). An identity servicemay be used to authorize a client and/or edge devices of a client inorder to establish a connection with the provider network and/or to useany particular service of the provider network. The provider network mayinclude database resources, other storage resources, compute resources,and/or any other type of data/video stream-processing resource. Any ofthe above resources/services may be configured based on the indicationfrom the edge device, as described herein.

In the example embodiment, a wireless infrastructure (e.g., wirelessinfrastructure 110) includes a “wireless mobile core” and any number of“mobile edge compute” networks. The edge client 114/edge device 106 maycommunicate 302 with the ABM service/provider network for variousconfiguration/control-related functions as described herein (e.g., via awide area network (e.g., the Internet) and/or other technique), withoutusing the wireless infrastructure. For example, the edge device may sendan indication of an upcoming video stream (e.g., bearer modification) tothe ABM service. As shown, the communication 302 is a made using a“logical” connection between edge client and ABM service. In otherwords, the edge client/edge device may communicate 302 through one ormore physical pathways using resources of a telco operator (e.g.,serving wireless infrastructure 110) and/or other Internet resources.

As shown, the edge device includes the edge client, a 4G radio, a 5Gradio, firmware, inference (e.g., one or more trained models), aDSP/GPU, and various other type of software. In embodiments, if a modeldetects/classifies a person with a dangerous weapon, then a rule(s) maycause a highest (or higher) definition video stream to be transmittedfrom the edge device to the ABM service via the wireless infrastructure.For example, as described herein, in response to the detection, the edgeclient/model may send an indication (e.g., communicate 302) to cause theABM service to configure one or more resources of the wirelessinfrastructure and/or provider network to process the upcoming datastream according to the highest (or higher) definition quality.

In embodiments, if a model of the edge device detects/classifies asuspect person, then a rule(s) may cause a high (but lower than thehighest) definition video stream to be transmitted from the edge deviceto the ABM service via the wireless infrastructure. For example, asdescribed herein, in response to the detection, the edge client/modelmay send an indication (e.g., communicate 302) to cause the ABM serviceto configure one or more resources of the wireless infrastructure and/orprovider network to process the upcoming data stream according to thehigh definition quality (e.g., lower definition than the higherdefinition used for detection of the weapon).

If a model of the edge device detects/classifies an animal, then arule(s) may cause a lower definition video stream to be transmitted fromthe edge device to the ABM service via the wireless infrastructure. Forexample, as described herein, in response to the detection, the edgeclient/model may send an indication (e.g., communicate 302) to cause theABM service to configure one or more resources of the wirelessinfrastructure and/or provider network to process the upcoming datastream according to the lower definition quality (e.g., lower definitionthan the high definition used for detection of the suspect person).

In some embodiments, if a model of the edge device detects/classifies achange in sunlight/clouds, blowing leaves, or other non-interestingmovement, then a rule(s) may prevent any video stream from beingtransmitted from the edge device to the ABM service via the wirelessinfrastructure (reducing unnecessary resource usage).

As depicted, the wireless infrastructure may include any number ofresources/services, such as analytics, policy management, subscribermanagement, exposure functions, slicing management, mobility management,gateway control plane, core user-plane, and resources/service on the MECnetworks. A shown, the ABM service may send a configuration request(e.g., via an interface) to the analytics, policy management, subscribermanagement, or exposure functions. However, in various embodiments, theABM service may send a configuration request (e.g., via an interface) toany of the above resources/services based on the indication from theedge device, as described herein.

Also depicted in the example embodiment are pathways for data plane/userplane traffic (e.g., for sending the data/video/audio stream via thewireless infrastructure) as well as control plane/signaling traffic(e.g., for sending other control signals between the providernetwork/wireless infrastructure and between the MECs/wirelessinfrastructure). As shown, some or all of a given data stream may betransmitted from the edge device to an LTE radio network and then to theprovider network via the core user-plane. Some or all of a given datastream may be transmitted from the edge device to an LTE radio networkand then to the provider network via an MEC. Some or all of a given datastream may be transmitted from the edge device to a 5G NR radio networkand then to the provider network via an MEC. In various embodiments, anyother suitable pathways through a wireless infrastructure may be used totransmit a given data stream from an edge device/data source to theprovider network via the wireless infrastructure.

Examples of different 4G network functions and different 5G networkfunctions are shown as part of different resources/services of thewireless infrastructure. However, in various embodiments, any of theresources/services may include any other number/type of functions thatmay be used to process data streams/data transmissions. In embodiments,the wireless infrastructure may include any other number ofresources/services to process data streams/data transmissions.

FIG. 4 is a logical block diagram illustrating an edge device that isused for edge intelligence-based resource modification to transmit datastreams to a provider network, according to some embodiments.

In the example embodiment, the edge device 106 includes a memory (e.g.,an operating memory) that implements the edge client 114 and one or moremodels 116. The memory also implements a stream and model manager 404.The stream and model manager 404 may perform any of the functionsdescribed herein for the edge device, such as generating and/or sendingan indication to the provider network, downloading and installing modelsfrom the provider network, and/or sending to the provider networkconfiguration/information associated with the edge device.

The edge device also includes one or more processors 406 (e.g., DSP,GPU) and one or more radios 408 (e.g., 4G radio, 5G radio, etc.) tocommunicate with the wireless infrastructure. The edge device alsoincludes (and/or is communicatively coupled to) one or more data sources410.

FIG. 5 is a logical block diagram illustrating an edge device that isused for edge intelligence-based resource modification to transmit datastreams to a provider network, according to some embodiments.

In the example embodiment, an edge device includes an RF transceiver anda wireless modem that communicates with an edge AI bearer modificationfunction (ABM function) via a wireless/3GPP stack. As shown, the ABMfunction may include any number of trained models from any number ofcatalogs. Each model may implement rules for objects. For example, if amodel detects a cat in video data, then the rule may be to not transmitany video stream to the provider network (or to transmit a lower qualitystream). If the model detects a suspect person in video data, then therule may be to transmit a video stream to the provider network using ahigher quality stream (or highest available quality stream). As shown,the ABM function may include the edge client as well as any number ofcommunication protocols/software, such as HTTP/CoAP/MQTT/LwM2M,TLS/DTLS, and TCP/UDP/IP.

As shown, the edge device may include application processors, DSPs, anML/AL interface, and may implement various protocols termination and atransport security layer. The edge device may also include OA&M,control, storage, and various peripherals (e.g., cameras, audio sensors(microphones), etc.).

FIG. 6 is a logical block diagram illustrating a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

In the depicted embodiment, an ABM service 104 of a provider network 102includes a resource manager 602 and an edge device coordinator 604. Edgedevice A 606 may send, to the ABM service, an indication of a quality ofupcoming video stream A and/or a configuration of one or more resources(e.g., of the wireless infrastructure 110 and/or provider network 102)based on a result of a model, as described herein. Edge device B 608 maysend, to the ABM service, an indication of results of a trained model,as described herein. As described above, the ABM service may configureone or more resources (e.g., of the wireless infrastructure 110 and/orprovider network 102) based on the received indication from edge deviceA and edge device B.

In some embodiments, edge device C 610 may send an indication of aquality of upcoming video stream C and/or of a configuration of one ormore resources the wireless infrastructure 110 based on a result of amodel at edge device C (instead of sending the indication to the ABMservice, as described above). This may allow resources at the wirelessinfrastructure to be configured faster (based on the indication of thequality of upcoming video stream C and/or of the configuration ofresources), when the edge device has permission and/or the capability todo so. In such embodiments, edge device C may also send, to the ABMservice, an indication of a configuration of one or more resources ofthe provider network. As described herein, this allows the ABM serviceto configure the one or more resources of the provider network toprocess upcoming video stream C. In the example embodiment edge deviceA, B and C may each include and/or be communicatively coupled to a videocamera.

In some embodiments, the ABM service may manage/coordinate (e.g., usingthe edge device coordinator 604) any number of edge devices to track amoving subject/object more efficiently and with faster response tostream videos. For example, based on receiving from edge device A anindication based on a result generated by a trained model at edge deviceA, the ABM service may send, to an interface of the serving wirelessinfrastructure, a request to configure one or more resources of theserving wireless infrastructure on behalf of edge device A in order totransmit data stream A from a data source of edge device A to theprovider network via the serving wireless infrastructure.

In response to receiving the indication from edge device A, the ABMservice may also identify, based on the indication received from edgedevice A, one or more additional edge devices (edge device B and C) thatare each expected to provide an additional data stream related to thedata stream (e.g., due to a determination by the ABM service that edgedevices B and C are within a threshold distance of edge device A and/orshare a geographical area with edge device A and/or edge device B and Care in a direction of movement of an object detected by edge device Aand are therefore expected to detect the same moving subject/object at anearby future time period.

In response to the identification of edge devices B and C as devicesthat are expected to provide an additional data stream related to thedata stream provided by edge device A, the ABM service may send, to theinterface of the serving wireless infrastructure, one or more additionalrequests to configure one or more resources of the serving wirelessinfrastructure on behalf edge devices B and C in order to transmit oneor more additional upcoming data streams from one or more additionaldata sources of edge devices B and C to the provider network via theserving wireless infrastructure. In embodiments, the ABM service maysend the requests to configure the one or more resources of the servingwireless infrastructure on behalf edge devices B and C according to thesame video quality and/or same configuration as was indicated for therequest that was sent on behalf of edge device A. This may allow an ABMservice to coordinate any number of data sources (e.g., video cameras attraffic lights or intersections) to more accurately track a movingobject (e.g., a high speed moving object).

In some embodiments, a given edge device/data source may send differentdata streams to the provider network via any number of different servingwireless infrastructures. For example, after transmitting a data streamto the provider network via a particular serving wireless infrastructure(e.g., telco operator network) based on a result of a trained model, theABM service may receive another indication based on another resultgenerated by the trained model. Based on the other indication receivedfrom the edge device, the ABM service may send, to an interface ofanother serving wireless infrastructure (e.g., a different telcooperator network), a request to configure one or more resources of theother serving wireless infrastructure on behalf of the edge device inorder to transmit another data stream from the data source to theprovider network via the other serving wireless infrastructure. Theabove process may be performed any number of times for any number ofdifferent serving wireless infrastructures. In embodiments, this mayallow the ABM service to continue to receive video streams from an edgedevice (e.g., high-priority streams), even if one or more telco operatornetworks becomes unavailable.

FIG. 7 is a high-level flowchart illustrating various methods andtechniques to implement model deployment for a system for edgeintelligence-based resource modification to transmit data streams to aprovider network, according to some embodiments. These techniques, aswell as the techniques discussed with regard to FIGS. 8-10, may beimplemented using components or systems as described above with regardto FIGS. 1-6, as well as other types of components or systems, and thusthe following discussion is not intended to be limiting as to the othertypes of systems that may implement the described techniques.

As indicated at block 702, an ABM service receives a configuration of anedge device (e.g., type of edge device/data source or environmentalconditions such as lighting/weather/time of day). In embodiments, theconfiguration may be received from an administrator user via a userinterface to the ABM service. In some embodiments, the configuration maybe received from the edge device.

At block 704, the ABM service determine, based on the configuration, oneor more trained models to be deployed to the edge device that arecompatible with the configuration (e.g., with the type of edgedevice/data source or environmental conditions such aslighting/weather/time of day). A given trained model may be configuredto generate a result based on processing data obtained at the edgedevice from a data source (e.g., video camera, microphone). At block706, the ABM service deploys the one or more trained models to the edgedevice, where they are installed and used.

In some embodiments, the ABM service may subsequently determine (via thean indication from the user interface or an indication directly from thedevice) one or more changes of a configuration of the edge device (e.g.,change in the lighting conditions at one or more video cameras thatprovide video data to the edge device and/or replacement of one or morevideo cameras that provide video data to the edge device with one ormore different types of cameras with different capabilities). Inresponse, the ABM service may determine, based on the receivedindication, a different trained model that is configured for the changedconfiguration/environment and deploy the different trained model to theedge device. The edge device may install and use it.

FIG. 8 is a high-level flowchart illustrating various methods andtechniques to implement a system for edge intelligence-based resourcemodification for transmitting data streams to a provider network,according to some embodiments.

At block 802, the ABM service receives, from an edge device of a remotenetwork, an indication based on a result generated by a trained model(e.g., results of the model, video quality/configuration of resources).At block 804, based on the indication received from the edge device, theABM service sends, to an interface of a serving wireless infrastructure,a request to configure one or more resources on behalf of the edgedevice in order to transmit a data stream (e.g., video, audio) from thedata source to the provider network via the serving wirelessinfrastructure.

At block 806, based on the indication received from the edge device, theABM service configures one or more resources of the provider network toprocess the data stream. At block 808, the data stream is processed bythe one or more resources of the provider network.

FIG. 9 is a high-level flowchart illustrating various methods andtechniques to implement an edge device for edge intelligence-basedresource modification to transmit data streams to a provider network,according to some embodiments.

At block 902, an edge device provides, to the ABM service, aconfiguration associated with the edge device. As discussed herein, insome embodiments, the configuration may instead by provided by a uservia a user interface. At block 904, the edge device receives, from theprovider network, one or more trained models that are compatible withthe configuration.

At block 906, the edge device obtains data from a data source. At block908, the edge device processes the obtained data to generate a result.At block 910, the edge device determines whether to send an indicationto the ABM service based on the result. If not, then the process returnsto block 906 to process additional obtained data.

At block 910, if the edge device determines to send an indication to theABM service based on the result, then at block 912, the edge devicesends, to the provider network, an indication based on the result,wherein the indication is used, by the provider network, to send arequest to a serving wireless infrastructure for configuration ofresources on behalf of the edge device. At block 914, the edge deviceinitiates transmission of the data stream to the provider network.

FIG. 10 is a high-level flowchart illustrating various methods andtechniques to manage multiple edge devices in a system for edgeintelligence-based resource modification for transmitting data streamsto a provider network, according to some embodiments.

At block 1002, the ABM service receives, from the edge device, anindication based on a result generated by a trained model (e.g., resultsof the model, video quality/configuration of resources). At block 1004,the ABM service identifies, based on the indication received from theedge device, one or more additional edge devices that are expected toprovide a data stream. At block 1006, based on the indication receivedfrom the edge device, the ABM service sends, to an interface of aserving wireless infrastructure, requests to configure one or moreresources on behalf of the edge device and additional edge devices inorder to transmit data streams (e.g., video, audio) from data sources tothe provider network via the serving wireless infrastructure.

At block 1008, based on the indication received from the edge device,the ABM service sends, configures one or more resources of the providernetwork to process the data streams. At block 1010, the ABM serviceprocesses the data streams by the one or more resources the providernetwork.

The methods described herein may in various embodiments be implementedby any combination of hardware and software. For example, in oneembodiment, the methods may be implemented by a computer system (e.g., acomputer system as in FIG. 11) that includes one or more processorsexecuting program instructions stored on a computer-readable storagemedium coupled to the processors. The program instructions may implementthe functionality described herein (e.g., the functionality of the ABMservice, edge client, models, and other components that implement thetechniques described herein). The various methods as illustrated in thefigures and described herein represent example embodiments of methods.The order of any method may be changed, and various elements may beadded, reordered, combined, omitted, modified, etc.

Embodiments to implement edge intelligence-based resource modificationfor transmitting data streams to a provider network as described hereinmay be executed on one or more computer systems, which may interact withvarious other systems or devices. One such computer system isillustrated by FIG. 11. In different embodiments, computer system 1100may be any of various types of devices, including, but not limited to, apersonal computer system, desktop computer, laptop, notebook, or netbookcomputer, mainframe computer system, handheld computer, workstation,network computer, a camera, a set top box, a mobile device, a consumerdevice, video game console, handheld video game device, applicationserver, storage device, a peripheral device such as a switch, modem,router, or in general any type of computing node or compute node,computing device, compute device, or electronic device.

In the illustrated embodiment, computer system 1100 includes one or moreprocessors 1110 coupled to a system memory 1120 via an input/output(I/O) interface 1130. Computer system 1100 further includes a networkinterface 1140 coupled to I/O interface 1130, and one or moreinput/output devices 1150, such as cursor control device 1160, keyboard1170, and display(s) 1180. Display(s) may include standard computermonitor(s) and/or other display systems, technologies or devices, in oneembodiment. In some embodiments, it is contemplated that embodiments maybe implemented using a single instance of computer system 1100, while inother embodiments multiple such systems, or multiple nodes making upcomputer system 1100, may host different portions or instances ofembodiments. For example, in one embodiment some elements may beimplemented via one or more nodes of computer system 1100 that aredistinct from those nodes implementing other elements.

In various embodiments, computer system 1100 may be a uniprocessorsystem including one processor 1110, or a multiprocessor systemincluding several processors 1110 (e.g., two, four, eight, or anothersuitable number). Processors 1110 may be any suitable processor capableof executing instructions, in one embodiment. For example, in variousembodiments, processors 1110 may be general-purpose or embeddedprocessors implementing any of a variety of instruction setarchitectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, orany other suitable ISA. In multiprocessor systems, each of processors1110 may commonly, but not necessarily, implement the same ISA.

In some embodiments, at least one processor 1110 may be a graphicsprocessing unit. A graphics processing unit or GPU may be considered adedicated graphics-rendering device for a personal computer,workstation, game console or other computing or electronic device, inone embodiment. Modern GPUs may be very efficient at manipulating anddisplaying computer graphics, and their highly parallel structure maymake them more effective than typical CPUs for a range of complexgraphical algorithms. For example, a graphics processor may implement anumber of graphics primitive operations in a way that makes executingthem much faster than drawing directly to the screen with a host centralprocessing unit (CPU). In various embodiments, graphics rendering may,at least in part, be implemented by program instructions for executionon one of, or parallel execution on two or more of, such GPUs. TheGPU(s) may implement one or more application programmer interfaces(APIs) that permit programmers to invoke the functionality of theGPU(s), in one embodiment.

System memory 1120 may store program instructions 1125 and/or dataaccessible by processor 1110, in one embodiment. In various embodiments,system memory 1120 may be implemented using any suitable memorytechnology, such as static random access memory (SRAM), synchronousdynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type ofmemory. In the illustrated embodiment, program instructions and dataimplementing desired functions, such as those described above (e.g., theABM service, edge client, models, etc.) are shown stored within systemmemory 1120 as program instructions 1125 and data storage 1135,respectively. In other embodiments, program instructions and/or data maybe received, sent or stored upon different types of computer-accessiblemedia or on similar media separate from system memory 1120 or computersystem 1100. A computer-accessible medium may include non-transitorystorage media or memory media such as magnetic or optical media, e.g.,disk or CD/DVD-ROM coupled to computer system 1100 via I/O interface1130. Program instructions and data stored via a computer-accessiblemedium may be transmitted by transmission media or signals such aselectrical, electromagnetic, or digital signals, which may be conveyedvia a communication medium such as a network and/or a wireless link,such as may be implemented via network interface 1140, in oneembodiment.

In one embodiment, I/O interface 1130 may be coordinate I/O trafficbetween processor 1110, system memory 1120, and any peripheral devicesin the device, including network interface 1140 or other peripheralinterfaces, such as input/output devices 1150. In some embodiments, I/Ointerface 1130 may perform any necessary protocol, timing or other datatransformations to convert data signals from one component (e.g., systemmemory 1120) into a format suitable for use by another component (e.g.,processor 1110). In some embodiments, I/O interface 1130 may includesupport for devices attached through various types of peripheral buses,such as a variant of the Peripheral Component Interconnect (PCI) busstandard or the Universal Serial Bus (USB) standard, for example. Insome embodiments, the function of I/O interface 1130 may be split intotwo or more separate components, such as a north bridge and a southbridge, for example. In addition, in some embodiments some or all of thefunctionality of I/O interface 1130, such as an interface to systemmemory 1120, may be incorporated directly into processor 1110.

Network interface 1140 may allow data to be exchanged between computersystem 1100 and other devices attached to a network, such as othercomputer systems, or between nodes of computer system 1100, in oneembodiment. In various embodiments, network interface 1140 may supportcommunication via wired or wireless general data networks, such as anysuitable type of Ethernet network, for example; viatelecommunications/telephony networks such as analog voice networks ordigital fiber communications networks; via storage area networks such asFibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 1150 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 1100, in oneembodiment. Multiple input/output devices 1150 may be present incomputer system 1100 or may be distributed on various nodes of computersystem 1100, in one embodiment. In some embodiments, similarinput/output devices may be separate from computer system 1100 and mayinteract with one or more nodes of computer system 1100 through a wiredor wireless connection, such as over network interface 1140.

As shown in FIG. 11, memory 1120 may include program instructions 1125that implement the various embodiments of the systems as describedherein, and data store 1135, comprising various data accessible byprogram instructions 1125, in one embodiment. In one embodiment, programinstructions 1125 may include software elements of embodiments asdescribed herein and as illustrated in the Figures. Data storage 1135may include data that may be used in embodiments (e.g., models, datastreams, indications, edge device identifiers, client identification andauthentication data, etc.). In other embodiments, other or differentsoftware elements and data may be included.

Those skilled in the art will appreciate that computer system 1100 ismerely illustrative and is not intended to limit the scope of theembodiments as described herein. In particular, the computer system anddevices may include any combination of hardware or software that canperform the indicated functions, including a computer, personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, a camera, a set top box, a mobile device, network device,internet appliance, PDA, wireless phones, pagers, a consumer device,video game console, handheld video game device, application server,storage device, a peripheral device such as a switch, modem, router, orin general any type of computing or electronic device. Computer system1100 may also be connected to other devices that are not illustrated, orinstead may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may in someembodiments be combined in fewer components or distributed in additionalcomponents. Similarly, in some embodiments, the functionality of some ofthe illustrated components may not be provided and/or other additionalfunctionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-readable mediumseparate from computer system 1100 may be transmitted to computer system1100 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. This computer readable storagemedium may be non-transitory. Various embodiments may further includereceiving, sending or storing instructions and/or data implemented inaccordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present invention may be practiced with othercomputer system configurations.

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM,non-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent example embodiments of methods. The methods may be implementedin software, hardware, or a combination thereof. The order of method maybe changed, and various elements may be added, reordered, combined,omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended that the invention embrace all such modifications and changesand, accordingly, the above description to be regarded in anillustrative rather than a restrictive sense.

1.-20. (canceled)
 21. A system, comprising: one or more processors of aprovider network; and one or more memories of the provider network,wherein the one or more memories have stored thereon instructions, whichwhen executed by the one or more processors, cause the one or moreprocessors to implement, for a client: receive, from an edge device of aremote network of the client, an indication based on a result generatedby the edge device, wherein the result is based on processing of dataobtained at the edge device from at least one data source; and based onthe indication received from the edge device, send, to an interface of aserving wireless infrastructure, a request to configure one or moreresources of the serving wireless infrastructure on behalf of the edgedevice in order to transmit a data stream from the data source to theprovider network via the serving wireless infrastructure.
 22. The systemas recited in claim 21, wherein the instructions, when executed by theone or more processors, cause the one or more processors to: based onthe indication received from the edge device, configure one or moreresources of the provider network to process the data stream.
 23. Thesystem as recited in claim 21, wherein the instructions, when executedby the one or more processors, cause the one or more processors to:determine, based on the indication from the edge device, a quality ofthe data stream to be transmitted; and configure one or more resourcesof the provider network to process the data stream according to thedetermined quality.
 24. The system as recited in claim 21, wherein theindication indicates one or more of: a quality of the data stream, aconfiguration of one or more resources of the provider network or theserving wireless infrastructure to process the data stream, or theresult generated by the edge device.
 25. The system as recited in claim21, wherein the instructions, when executed by the one or moreprocessors, cause the one or more processors to: receive, from the edgedevice, another indication based on another result generated by the edgedevice; and based on the indication received from the edge device, send,to an interface of another serving wireless infrastructure, a request toconfigure one or more resources of the other serving wirelessinfrastructure on behalf of the edge device in order to transmit anotherdata stream from the data source to the provider network via the otherserving wireless infrastructure.
 26. The system as recited in claim 21,wherein the instructions, when executed by the one or more processors,cause the one or more processors to: receive, from the edge device,another indication based on another result generated by the edge device;and based on the other indication received from the edge device, send,to the interface of the serving wireless infrastructure, another requestto release at least one of the one or more resources of the servingwireless infrastructure, wherein the at least one released resource isavailable to be configured for transmission of a different data stream.27. A method, comprising: performing, by one or more computing devicesof a provider network: receiving, from an edge device of a remotenetwork of a client, an indication based on a result generated by theedge device, wherein the result is based on processing of data obtainedat the edge device from at least one data source; and based on theindication received from the edge device, sending, to an interface of aserving wireless infrastructure, a request to configure one or moreresources of the serving wireless infrastructure on behalf of the edgedevice in order to transmit a data stream from the data source to theprovider network via the serving wireless infrastructure.
 28. The methodas recited in claim 27, further comprising: based on the indicationreceived from the edge device, configuring one or more resources of theprovider network to process the data stream.
 29. The method as recitedin claim 27, further comprising: determining, based on the indicationfrom the edge device, a quality of the data stream to be transmitted;configuring one or more resources of the provider network to process thedata stream according to the determined quality; and sending anotherindication to the edge device or the data source, wherein the otherindication causes the data source to transmit the data stream accordingto the determined quality.
 30. The method as recited in claim 27,wherein the indication indicates one or more of: a quality of the datastream, a configuration of one or more resources of the provider networkor the serving wireless infrastructure to process the data stream, orthe result generated by the edge device.
 31. The method as recited inclaim 27, further comprising: identifying, based on the indicationreceived from the edge device, one or more additional edge devices thatare each expected to provide an additional data stream related to thedata stream; and sending, to the interface of the serving wirelessinfrastructure, one or more additional requests to configure one or moreresources of the serving wireless infrastructure on behalf the one ormore additional edge devices in order to transmit one or more additionaldata streams from one or more additional data sources to the providernetwork via the serving wireless infrastructure.
 32. The method asrecited in claim 27, further comprising: receiving, from the edgedevice, another indication based on another result generated by the edgedevice; and based on the indication received from the edge device,sending, to an interface of another serving wireless infrastructure, arequest to configure one or more resources of the other serving wirelessinfrastructure on behalf of the edge device in order to transmit anotherdata stream from the data source to the provider network via the otherserving wireless infrastructure.
 33. The method as recited in claim 27,wherein the data stream comprises video data, and further comprising:receiving, from the edge device, another indication based on anotherresult generated by the edge device; and based on the other indicationreceived from the edge device, sending, to the interface of the servingwireless infrastructure, another request to release at least one of theone or more resources of the serving wireless infrastructure, whereinthe at least one released resource is available to be configured fortransmission of a different data stream.
 34. An edge device, comprising:one or more processors; and one or more memories, wherein the one ormore memories have stored thereon instructions, which when executed bythe one or more processors, cause the one or more processors toimplement an edge client, wherein the edge client is configured to:obtain data from at least one data source; process the obtained data togenerate a result; send, to a remote provider network, an indicationbased on the result, wherein the indication is configured to be used, bythe remote provider network, to send a request to an interface of aserving wireless infrastructure for configuration of one or moreresources on behalf of the edge device in order to transmit a datastream from the data source to the remote provider network via theserving wireless infrastructure; and subsequent to the configuration ofthe one or more resources of the serving wireless infrastructure onbehalf of the edge device, initiate transmission of the data stream fromthe data source to the remote provider network via the serving wirelessinfrastructure.
 35. The edge device as recited in claim 34, wherein theindication is configured to be used, by the remote provider network, toconfigure one or more resources of the remote provider network toprocess the data stream.
 36. The edge device as recited in claim 34,wherein the indication indicates one or more of: a quality of the datastream, a configuration of one or more resources of the remote providernetwork or the serving wireless infrastructure to process the datastream, or the result generated by the edge client.
 37. The edge deviceas recited in claim 34, wherein the edge client is further configuredto: obtain the data from a plurality of different video cameras ormicrophones.
 38. The edge device as recited in claim 34, wherein theedge client is further configured to: determine one or more changes of aconfiguration or environment of the edge device; send, to the remoteprovider network, an indication of the changed configuration orenvironment; and receive, from the remote provider network, a model,wherein the model is configured for the changed configuration orenvironment.
 39. The edge device as recited in claim 38, wherein the oneor more changes comprises a change in lighting conditions at one or morevideo cameras that provide video data to the edge device.
 40. The edgedevice as recited in claim 38, wherein the one or more changes comprisesa replacement of one or more video cameras that provide video data tothe edge device.